Yong-Tak Kim , Jae-Ung Yu , Tae-Woong Kim , Hyun-Han Kwon
{"title":"气候变化模型多模型集合的新方法:关于自然变异性与历史和未来气候代表性的观点","authors":"Yong-Tak Kim , Jae-Ung Yu , Tae-Woong Kim , Hyun-Han Kwon","doi":"10.1016/j.wace.2024.100688","DOIUrl":null,"url":null,"abstract":"<div><p>This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.</p></div>","PeriodicalId":48630,"journal":{"name":"Weather and Climate Extremes","volume":"44 ","pages":"Article 100688"},"PeriodicalIF":6.1000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212094724000495/pdfft?md5=d3790effe8ae3930e8a96ca6b8068dfc&pid=1-s2.0-S2212094724000495-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate\",\"authors\":\"Yong-Tak Kim , Jae-Ung Yu , Tae-Woong Kim , Hyun-Han Kwon\",\"doi\":\"10.1016/j.wace.2024.100688\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.</p></div>\",\"PeriodicalId\":48630,\"journal\":{\"name\":\"Weather and Climate Extremes\",\"volume\":\"44 \",\"pages\":\"Article 100688\"},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2212094724000495/pdfft?md5=d3790effe8ae3930e8a96ca6b8068dfc&pid=1-s2.0-S2212094724000495-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Weather and Climate Extremes\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212094724000495\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Weather and Climate Extremes","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212094724000495","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate
This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.
期刊介绍:
Weather and Climate Extremes
Target Audience:
Academics
Decision makers
International development agencies
Non-governmental organizations (NGOs)
Civil society
Focus Areas:
Research in weather and climate extremes
Monitoring and early warning systems
Assessment of vulnerability and impacts
Developing and implementing intervention policies
Effective risk management and adaptation practices
Engagement of local communities in adopting coping strategies
Information and communication strategies tailored to local and regional needs and circumstances